[1]} apply a causal analysis on the problem of domain generalization in segmentation of medical images. They first simulate shifted domain images via a randomly weighted shallow network; then they intervene upon the images such that spurious correlations are removed and finally train their segmentation model while enforcing a domain invariance condition. [2]} develop a method to reuse adversarial mask discriminators for test-time training to combat distribution shifts in medical image segmentation tasks. In their discussion of their method they explain the good performance of their method under a causal lens. Finally [3]} build a causal Bayesian prior to aide MRI tissue segmentation to generalize across different medical centers.
